{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import scipy.io as sio\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "colorstyle=dict(red_face = np.array([255,85,65])/255, red_edge = np.array([201,67,52])/255,\n",
    "                blue_face = np.array([49,115,255])/255, blue_edge = np.array([36,85,189])/255,\n",
    "                green_face= np.array([84,224,81])/255, green_edge= np.array([62,166,60])/255,\n",
    "                yellow_face=np.array([255,207,49])/255,yellow_edge=np.array([191,155,36])/255,\n",
    "                gray_face=np.array([169,169,169])/255,gray_edge=np.array([137,137,137])/255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "black_classic_edge=np.array([0.15,0.15,0.15])*1.5\n",
    "black_classic_face=np.array([0.4,0.4,0.4])*1.5\n",
    "\n",
    "light_blue_edge=1-(1-colorstyle['blue_face'])*0.7\n",
    "\n",
    "light_blue_face=1-(1-colorstyle['blue_face'])*0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataRead=sio.loadmat('FigS5.mat',squeeze_me=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "TTilde_1=DataRead['TTilde_1']\n",
    "TTilde_1_err_left=DataRead['TTilde_1_err_left']\n",
    "TTilde_1_err_right=DataRead['TTilde_1_err_right']\n",
    "gamma_1=DataRead['gamma_1']\n",
    "gamma_1_err=DataRead['gamma_1_err']\n",
    "alpha_p_1=DataRead['alpha_p_1']\n",
    "alpha_p_1_err=DataRead['alpha_p_1_err']\n",
    "\n",
    "TTilde_EOS=DataRead['TTilde_EOS']\n",
    "gamma_EOS=DataRead['gamma_EOS']\n",
    "alpha_p_EOS=DataRead['alpha_p_EOS']\n",
    "\n",
    "TTilde_2=DataRead['TTilde_2']\n",
    "TTilde_2_err_left=DataRead['TTilde_2_err_left']\n",
    "TTilde_2_err_right=DataRead['TTilde_2_err_right']\n",
    "\n",
    "c20=DataRead['c20']\n",
    "c2=DataRead['c2']\n",
    "c2_err=DataRead['c2_err']\n",
    "\n",
    "c10=DataRead['c10']\n",
    "c1=DataRead['c1']\n",
    "c1_err=DataRead['c1_err']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_offset=0.05\n",
    "y_offset=0.1\n",
    "box_height=0.65\n",
    "box_width=0.375\n",
    "box_spacing_h=0.15\n",
    "\n",
    "fig,axs=plt.subplots(nrows=1,ncols=2,figsize=[4,2])\n",
    "\n",
    "axs[0].set_position([x_offset,y_offset,box_width,box_height])\n",
    "axs[1].set_position([x_offset+box_spacing_h+box_width,y_offset,box_width,box_height])\n",
    "\n",
    "axs[0].tick_params(axis='both',direction='in',right=1,top=1,length=2.5,labelsize=9)\n",
    "axs[1].tick_params(axis='both',direction='in',right=1,top=1,length=2.5,labelsize=9)\n",
    "\n",
    "plt.sca(axs[0])\n",
    "plt.errorbar(TTilde_1,gamma_1-1,yerr=gamma_1_err,xerr=[TTilde_1_err_left,TTilde_1_err_right],marker='o',linestyle='None',label='Fitted Value',\n",
    "            markersize=3,elinewidth=0.75,ecolor=colorstyle['blue_edge'],mec=colorstyle['blue_edge'],mfc=colorstyle['blue_face'])\n",
    "\n",
    "plt.plot(TTilde_EOS,gamma_EOS-1,'o',alpha=0.25,label='2012 EoS',markersize=3,color='k')\n",
    "plt.axvline(0.167,linestyle='--',color='k',linewidth=0.75)\n",
    "plt.fill_between([0.167+0.01,0.167-0.01],[50,50],[-50,-50],color=[0.5,0.5,0.5],alpha=0.2,linewidth=0)\n",
    "plt.ylim([-0.02,0.9])\n",
    "plt.xlim([0.05,0.18])\n",
    "plt.xlabel(r'$T/T_\\mathrm{F}$',fontsize=9,labelpad=1)\n",
    "plt.ylabel(r'$\\gamma-1$',fontsize=9,labelpad=0)\n",
    "\n",
    "plt.sca(axs[1])\n",
    "plt.errorbar(TTilde_1,alpha_p_1,yerr=alpha_p_1_err,xerr=[TTilde_1_err_left,TTilde_1_err_right],marker='o',linestyle='None',label='Fitted Value',\n",
    "            markersize=3,elinewidth=0.75,ecolor=colorstyle['blue_edge'],mec=colorstyle['blue_edge'],mfc=colorstyle['blue_face'])\n",
    "\n",
    "plt.plot(TTilde_EOS,alpha_p_EOS,'o',alpha=0.25,label='2012 EoS',markersize=3,color='k')\n",
    "plt.axvline(0.167,linestyle='--',color='k',linewidth=0.75)\n",
    "plt.fill_between([0.167+0.01,0.167-0.01],[50,50],[-50,-50],color=[0.5,0.5,0.5],alpha=0.2,linewidth=0)\n",
    "\n",
    "plt.xlim([0.05,0.18])\n",
    "plt.ylim([0.1,0.8])\n",
    "plt.xlabel(r'$T/T_\\mathrm{F}$',fontsize=9,labelpad=1)\n",
    "plt.ylabel(r'${k_\\mathrm{B} T \\alpha_p}/{c_V}$',fontsize=9,labelpad=1)\n",
    "\n",
    "fig.savefig('FigS5AB.pdf',dpi=300)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_offset=0.15\n",
    "y_offset=0.1\n",
    "\n",
    "vspacing=0.03\n",
    "box_width=0.75\n",
    "box_height=0.62\n",
    "\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True,figsize=[2.,2])\n",
    "\n",
    "ax1.tick_params(axis='both',direction='in',top=1,right=1,labelsize=7)\n",
    "ax2.tick_params(axis='both',direction='in',top=1,right=1,labelsize=7)\n",
    "\n",
    "\n",
    "plt.sca(ax1)\n",
    "\n",
    "plt.plot(TTilde_2,c10,marker='d',markersize=2.5,markeredgewidth=0.5,color=colorstyle['gray_face'],\n",
    "         mec=colorstyle['gray_edge'],linestyle='None',zorder=2)\n",
    "\n",
    "plt.errorbar(TTilde_2,c1,xerr=[TTilde_2_err_left,TTilde_2_err_right],yerr=c1_err,\n",
    "             marker='o',linestyle='None',markersize=2.5,color=black_classic_edge,\n",
    "             mec=black_classic_edge,mfc=black_classic_edge,markeredgewidth=0.5,elinewidth=0.5,zorder=1)\n",
    "\n",
    "\n",
    "\n",
    "plt.ylim([0.385,0.485])\n",
    "# plt.axvline(0.167,linestyle='--',color='k',label='Tc',linewidth=0.75)\n",
    "\n",
    "ax1.set_yticks([0.4,0.45])\n",
    "\n",
    "plt.sca(ax2)\n",
    "\n",
    "\n",
    "plt.plot([0.167,0.21],[0,0],'-.',color='k',linewidth=0.75)\n",
    "\n",
    "\n",
    "plt.plot(TTilde_2,c20,marker='d',markersize=2.5,markeredgewidth=0.5,\n",
    "         color=light_blue_face,mec=light_blue_edge,linestyle='None',zorder=2,alpha=0.7)\n",
    "\n",
    "plt.errorbar(TTilde_2,c2,xerr=[TTilde_2_err_left,TTilde_2_err_right],yerr=c2_err,\n",
    "             marker='o',linestyle='None',markersize=2.5,color=colorstyle['blue_edge'],\n",
    "             mec=colorstyle['blue_edge'],mfc=colorstyle['blue_edge'],markeredgewidth=0.5,elinewidth=0.5,label='Sonogram',zorder=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.xlim([0.05,0.18])\n",
    "plt.ylim([0.05,0.13])\n",
    "\n",
    "ax1.spines['bottom'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax1.xaxis.tick_top()\n",
    "# ax1.tick_params(labeltop='off')  # don't put tick labels at the top\n",
    "ax2.xaxis.tick_bottom()\n",
    "\n",
    "ylim1L,ylim1U=ax1.get_ylim()\n",
    "ylim2L,ylim2U=ax2.get_ylim()\n",
    "\n",
    "yrange1=ylim1U-ylim1L\n",
    "yrange2=ylim2U-ylim2L\n",
    "\n",
    "ax1.set_position([x_offset,y_offset+box_height/(yrange1+yrange2)*yrange2+vspacing,box_width,box_height/(yrange1+yrange2)*yrange1])\n",
    "ax2.set_position([x_offset,y_offset,box_width,box_height/(yrange1+yrange2)*yrange2])\n",
    "\n",
    "d = .02  # how big to make the diagonal lines in axes coordinates\n",
    "# arguments to pass to plot, just so we don't keep repeating them\n",
    "\n",
    "ax_width=ax1.get_position().width\n",
    "ax1_height=ax1.get_position().height\n",
    "ax2_height=ax2.get_position().height\n",
    "\n",
    "kwargs = dict(transform=ax1.transAxes, color='k', clip_on=False)\n",
    "ax1.plot((-d, +d), (-d*ax_width/ax1_height, +d*ax_width/ax1_height), **kwargs,linewidth=0.75)        # top-left diagonal\n",
    "ax1.plot((1 - d, 1 + d), (-d*ax_width/ax1_height, +d*ax_width/ax1_height), **kwargs,linewidth=0.75)  # top-right diagonal\n",
    "\n",
    "kwargs.update(transform=ax2.transAxes)  # switch to the bottom axes\n",
    "ax2.plot((-d, +d), (1 - d*ax_width/ax2_height, 1 + d*ax_width/ax2_height), **kwargs,linewidth=0.75)  # bottom-left diagonal\n",
    "ax2.plot((1 - d, 1 + d), (1 - d*ax_width/ax2_height, 1 + d*ax_width/ax2_height), **kwargs,linewidth=0.75)  # bottom-right diagonal\n",
    "\n",
    "plt.plot([0.167,0.167],[ylim2L,ylim2L+(yrange1+yrange2)/box_height*(box_height+vspacing)],linestyle='--',color='k',linewidth=0.75,clip_on=False)\n",
    "plt.fill_between([0.167+0.01,0.167-0.01],[ylim2L+(yrange1+yrange2)/box_height*(box_height+vspacing),ylim2L+(yrange1+yrange2)/box_height*(box_height+vspacing)],[ylim2L,ylim2L],color=[0.5,0.5,0.5],clip_on=False,alpha=0.2,linewidth=0)\n",
    "\n",
    "# plt.yticks([0,0.05,0.10])\n",
    "# fig.subplots_adjust(hspace=0.075)\n",
    "\n",
    "plt.xlabel(r'$T/T_\\mathrm{F}$',fontsize=9,labelpad=1)\n",
    "plt.ylabel(r'$c/v_\\mathrm{F}$',fontsize=9,labelpad=1)\n",
    "\n",
    "fig.savefig('FigS5C.pdf',dpi=300)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(TTilde_1,gamma_1-1,yerr=gamma_1_err,xerr=[TTilde_1_err_left,TTilde_1_err_right],marker='o',linestyle='None',label='Fitted Value',\n",
    "            markersize=3,elinewidth=0.75,ecolor=colorstyle['blue_edge'],mec=colorstyle['blue_edge'],mfc=colorstyle['blue_face'])\n"
   ]
  },
  {
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